
Multi-view Stereo. Traditional MVS studies can be divided into three categories: volumetric, point cloud-based, and depth map-based methods [1]}. Comparatively, depth map-based methods are more concise and flexible, they estimate depth maps for all reference images and then perform depth fusion to achieving a 3D model [2]}, [3]}, [4]}. The popular MVS methods in this category are usually based on PatchMatch Stereo algorithm [5]}. Recently, many studies have applied a learning-based approach, which has achieved significant performance improvements over traditional methods. Several volumetric methods [6]}, [7]} employed 3D CNNs to explicitly predict global 3D surfaces. [8]} proposed a depth-map-based method, MVSNet, which is an end-to-end trainable architecture with three steps. However, these methods still confronted the matching ambiguity problem and could not process high-resolution images due to limited memory. Many methods based on the three-step architecture of MVSNet are proposed to address these issues. Several studies [9]}, [10]}, [11]}, [12]}, [13]} applied a coarse-to-fine framework which estimates the depths through multiple stages to reduce the computational complexity. They used the estimated depth of each stage to adjust the depth hypothesis range for cost formulation in the next stage.
To enhance the matching cost volume, other methods introduced a novel strategy for cost volume formulation. [14]} proposed a patch-wise feature matching instead of pixel-wise matching to compute the cost. [15]} and [16]} applied an attention technique to improve feature matching. [13]}, [18]} estimated visibility information to score the view weights for cost aggregation.
